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End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving
[article]
2021
arXiv
pre-print
Inspired by the UNet architecture of semantic image segmentation, we propose a lightweight UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane detection and path prediction (PP) in autonomous driving. We also design and integrate a PP algorithm with convolutional neural network (CNN) to form a simulation model (CNN-PP) that can be used to assess CNN's performance qualitatively, quantitatively, and dynamically in a host agent car driving along with other agents
arXiv:2102.04738v2
fatcat:lso75tzjcrehhdtcd2wt6oxe6q